TY - INPR A1 - Böhm, Stefan-Andreas A1 - Zagar, Bare Luka A1 - Riß, Fabian A1 - Kortüm, Christian A1 - Knoll, Alois Christian T1 - Automated Feature Recognition in Surface Cad Models Based on Graph Neural Networks N2 - Driven by increasing customer demands, manufacturing processes encompass increasingly intricate workflows. The industry relies on computer-aided process planning software to effectively manage the intricacies of these complex manufacturing processes. This software is crucial to analyze computer-aided design data for a product and determining the required machining steps. However, a notable challenge arises, particularly in the case of custom products, where the machining steps can significantly vary depending on the available machinery and the employees' preferences.This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can encode their insights and expertise within this framework using a configurable synthetic data generator. Modern graph neural networks learn from the data generated by this data generator, achieving an average recognition accuracy up to 80% (F1-score) with a runtime performance of 21.98 milliseconds per model. Importantly, it accomplishes this even when confronted with highly intersecting machining features without requiring data conversion into alternative formats, such as voxel or pixel representations. KW - 3D deep learning KW - CAD KW - Graph neural networks KW - Machining feature recognition KW - Snthetic data generation KW - Surface models Y1 - 2024 UR - https://opus4.kobv.de/opus4-rosenheim/frontdoor/index/index/docId/2455 ER -